Abstract: Systems and methods are provided for automatically generating a program based on a natural language utterance using semantic parsing. The semantic parsing includes translating a natural language utterance into instructions in a logical form for execution. The methods use a pre-trained natural language model and generate a canonical utterance as an intermediate form before generating the logical form. The natural language model may be an auto-regressive natural language model with a transformer to paraphrase a sequence of words or tokens in the natural language utterance. The methods generate a prompt including exemplar input/output pairs as a few-shot learning technique for the natural language model to predict words or tokens. The methods further use constrained decoding to determine a canonical utterance, iteratively selecting sequence of words as predicted by the model against rules for canonical utterances. The methods generate a program based on the canonical utterance for execution in an application.

Abstract: Implementations of semantic parsing using pre-trained language models are provided. One aspect includes a computing system for semantic parsing of natural language.

Abstract: Systems and methods are provided for implementing conversational large language model (“LLM”) or other AI/ML-based user tenant orchestration. A first prompt is generated based on natural language (“NL”) input from a user. The first prompt is used by a first LLM or AI/ML system to generate a query to access data items that are stored in a portion of a multitenant data storage system, the portion being accessible by the user. Once accessed and received, the data items are input into a second prompt that is used by a second LLM or AI/ML system to return a set of functions with corresponding sets of arguments. The set of functions are executed according to the sets of arguments, and the results of the executed functions are used to generate a response to the NL input. The generated response is then caused to be presented to the user via a user interface.

Abstract: Examples are disclosed that related to synthesizing a dataset of utterances in an automated manner using a computer while preserving user privacy. The synthesized dataset of utterances is usable to train a machine learning model. In one example, a differentially private parse tree generation model is trained based at least on private parse trees of a private utterance-parse tree dataset. A differentially private parse-to-utterance model is trained based at least on private utterances and corresponding private parse trees of the private utterance-parse tree dataset. A synthesized parse tree dataset is generated. The synthesized parse tree dataset includes synthesized parse trees sampled at random from the trained differentially private parse tree generation model. A synthesized utterance dataset is generated, via the trained differentially private parse-to-utterance model.

Abstract: Aspects of the present disclosure relate to a machine learning-based dialogue authoring environment. In examples, a developer or creator of a virtual environment may use a generative multimodal machine learning (ML) model to create or otherwise update aspects of a dialogue tree for one or more computer-controlled agents and/or players of the virtual environment. For example, the developer may provide an indication of context associated with the dialogue for use by the ML model, such that the ML model may generate a set of candidate interactions accordingly. The developer may select a subset of the candidate interactions for inclusion in the dialogue tree, which may then be used to generate associated nodes within the tree accordingly. Thus, nodes in the dialogue tree may be iteratively defined based on model output of the ML model, thereby assisting the developer with dialogue authoring for the virtual environment.

Abstract: Systems and methods are provided for automatically generating a program based on a natural language utterance using semantic parsing. The semantic parsing includes translating a natural language utterance into instructions in a logical form for execution. The methods use a pre-trained natural language model and generate a canonical utterance as an intermediate form before generating the logical form. The natural language model may be an auto-regressive natural language model with a transformer to paraphrase a sequence of words or tokens in the natural language utterance. The methods generate a prompt including exemplar input/output pairs as a few-shot learning technique for the natural language model to predict words or tokens. The methods further use constrained decoding to determine a canonical utterance, iteratively selecting sequence of words as predicted by the model against rules for canonical utterances. The methods generate a program based on the canonical utterance for execution in an application.

Abstract: The present invention proposed manufacturing device of coating layers with good conductivity and corrosion resistance at high productivity comprising etching the oxide layer on the stainless steel substrate by plasma etching to activate the surface and prevent from decreasing it's conductivity, coating metal nitrides like CrN or TiN in nano size thickness on the etched surface and coating carbon layer at nano size thickness on top of it. According to the present invention, it is possible to produce manufacture fuel cell bipolar plate, electrode material and stainless steel with reinforced conductivity and corrosion resistance in mass.